idea. Now let me tell you a story about how I've used this. So this is a kind of a fun and interesting challenge called parameter golf. It's a benchmark that set up that was put up by Open AI. And it tests models ability to effectively do kind of ML research. So it asks the model to basically take a small um kind of model and train it in with eight with eight 8100 GPUs in less than 10 minutes. And what you see on the Y is basically you can think about it as loss. So lower is better, okay? And what I did was I set up a kind of a verifier loop using manage agents and outcomes to test the ability for Opus 4.7 and one of our frontier models like mythos class models on this task. And what you see is basically I allow the model to continue to iterate until the outcome that I specify is is satisfied which is it finished exactly 20 iterations and kind of it kind of met all the experimental criteria as defined by the benchmark. What you see is the frontier capability models are extremely good with this pattern of kind of loops in software and and kind of verification because what happens is instead of encoding steering me and into like me as the human, you're encoding the signal into the environment. So, the model can self-correct when it receives feedback from, for example, the verifier. And using this kind of paradigm with very high-capacity models, you can get very strong results. So, the main point I'm trying to make here is that this paradigm of loops, which a lot of people been talking about today, paired with very capacity models is a very good general primitive for long-running asynchronous work. That's really the key point here.